다항 분포

노트

위키데이터

• ID : Q1147928

말뭉치

1. The multinomial distribution is the type of probability distribution used to calculate the outcomes of experiments involving two or more variables.
2. The multinomial distribution would allow us to calculate the probability that the above combination of outcomes will occur.
3. (X 1 , ..., X k ) follows a multinomial distribution with parameters n and p, where p = (p 1 , ..., p k ).
4. The goal of equivalence testing is to establish the agreement between a theoretical multinomial distribution and observed counting frequencies.
5. Let q {\displaystyle q} denote a theoretical multinomial distribution and let p {\displaystyle p} be a true underlying distribution.
6. Multinomial distribution, in statistics, a generalization of the binomial distribution, which admits only two values (such as success and failure), to more than two values.
7. Like the binomial distribution, the multinomial distribution is a distribution function for discrete processes in which fixed probabilities prevail for each independently generated value.
8. His study of the resulting multinomial distribution led him to discover the basic principles of genetics.
9. 4.1, we expect to see a corresponding progression in this sequence of priors — as is perhaps best seen from the above example with the multinomial distribution.
10. For such tests we only have to assume a multinomial distribution of the profiles (possible score patterns for an object/product), which is true in case of random sampling.
11. Numerous ways to construct such tests were proposed over the years, and one of the possible approaches is based on the multinomial distribution.
12. Usually, it is clear from context which meaning of the term multinomial distribution is intended.
13. The multinomial distribution is preserved when the counting variables are combined.
14. The multinomial distribution is also preserved when some of the counting variables are observed.
15. The Multinomial distribution arises as a model for the following experimental situation.
16. The multinomial distribution can be used to compute the probabilities in situations in which there are more than two possible outcomes.
17. Definition 11.1 (Multinomial distribution) Consider \(J\) categories.
18. Parameter Multinomial distribution uses the following parameter.
19. The term ‘multinomial distribution’ was introduced by Sir Ronald Fisher in 1925.
20. The multinomial distribution appears in the following probability scheme.
21. In situations where you are calculating the probability of events where there are more than 2 outcomes, the multinomial distribution might be applicable.
22. The first example will involve a probability that can be calculated either with the binomial distribution or the multinomial distribution.
23. While the binomial distribution is a more well-known discrete distribution, the multinomial distribution provides a useful generalization to the binomial distribution.
24. The text Categorical Data Analysis by Alan Agresti contains more details on both the aforementioned regression models and more details about the multinomial distribution.

메타데이터

위키데이터

• ID : Q1147928

Spacy 패턴 목록

• [{'LOWER': 'multinomial'}, {'LEMMA': 'distribution'}]